Methods for improving the quality of computed tomography image series by image processing are widely known. Reference is made in an example fashion to the document DE 10 2005 038 940 A1, in which an edge-maintaining filter is used to improve the image. The articles by P. Perona and J. Malik, Scale space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, pp. 629-639, 1990; and J. Weichert, Anisotropic Diffusion Filtering in Image Processing, Teubner-Verlag, Stuttgart, Germany, 1998 use diffusion filters in order to improve the image quality. Reference is also made to the document DE 10 2005 012 654 A1, in which image data is filtered with the aid of correlation calculations so as to also improve the quality in this case.
However, all the abovementioned known methods for improving the quality of images by image processing reach their limits when the relevant contrast is in the region of or even smaller than the noise. If CT perfusion examinations of certain organs, such as brain, liver or heart, are considered, it can be shown that the typical changes in the CT value required for detecting the perfusion are in the range from approximately 2 to 20 HU, that is to say 0.2 to 2% of the contrast between water and air. Hence the pixel noise plays a decisive part.